The invention is related generally to digital signal processing and, in particular, digital signal processing of images.
Noise is an inherent feature in any communication system, and efforts to reduce noise are continually being explored. For example, it is well known that low-pass filters with pre-set frequency ranges reduce high-frequency noise. Moreover, it is known that in many communication systems it is advantageous to reduce noise prior to displaying the signal (or image) because noise reduction improves image display quality.
Modern-day image processors also use other techniques to enhance image display. For example, image processes utilize compression (or encoding) techniques to minimize the amount of memory needed to process images. There are compression standards for still images (or pictures), color facsimile (fax) machines, medical imaging systems, video cassette records (VCRs), entertainment systems, televisions (TVs), high-definition TVs (HDTVs), World-Wide Web browsers, and for personal computer (PC) displays, for example.
The coding efficiency of an image processor can be improved if system noise is reduced prior to encoding. Adaptive filters have been applied to images for this purpose. For example, adaptive filters reduce noise by monitoring the communication system and feeding back communication system information to automatically and dynamically adjust their filtering characteristics. Generally, adaptive filters can be obtained using non-linear approaches, such as median-based filters or filters having variable behavior according to the estimation of certain system or image parameters. For example, some adaptive filters are obtained by estimating the temporal correlation of certain parameters.
These adaptive filters have limitations, however. For example, if a video signal is greatly affected by noise, these adaptive filters confuse the undesirable noise with image motion. This limitation causes the fine details of the image to be attenuated, or smoothed, which also is undesirable. Proposed solutions include variable-strength filters, i.e., filters with curved selectivity. Arbitrary curve selection, however, affects the temporal frequency of the adaptive filter, resulting in the so-called xe2x80x9ccomet effect,xe2x80x9d which is more annoying than detail smoothing. It can be appreciated therefore that what is needed is an image enhancement technique that reduces noise without blurring fine details of the image or affecting the temporal frequency.
Presented herein is a system and method for reducing noise using recursive noise level estimation. An example embodiment includes a noise filter that uses a processing window with a target pixel and several neighboring pixels. The noise filter includes an image processor that generates a replacement value for the target pixel in the processing window based on a weighted average of the absolute values of degrees of similarity between the target pixel and the plurality of neighboring pixels. The noise filter recursively processes the noise levels to adapt to the local features of the scanned image, as well as to adapt to changing noise levels of neighboring pixels. That is the noise filter generates recursive noise level estimations whereby local feature data is fed back into the filter for calibration. The noise filter uses fuzzy logic processing to filter noise without smoothing the image.
Further features and advantages of the invention as well as the structure and operation of various embodiments are described in detail below.